Cassava Leaf Disease Classification
Identify the type of disease present on Cassava Leaf image
This notebook is a simple training pipeline in TensorFlow for the Cassava Leaf Competition where we are given 21,397 labeled images of cassava leaves classified as 5 different groups (4 diseases and a healthy group) and asked to predict on unseen images of cassava leaves. As with most image classification problems, we can use and experiment with many different forms of augmentation and we can explore transfer learning.
%%writefile conditional_cell_extension.py
def run_if(line, cell=None):
'''Execute current line/cell if line evaluates to True.'''
if not eval(line):
return
get_ipython().ex(cell)
def load_ipython_extension(shell):
'''Registers the run_if magic when the extension loads.'''
shell.register_magic_function(run_if, 'line_cell')
def unload_ipython_extension(shell):
'''Unregisters the run_if magic when the extension unloads.'''
del shell.magics_manager.magics['cell']['run_if']
%reload_ext conditional_cell_extension
import numpy as np
import pandas as pd
import seaborn as sns
import albumentations as A
import matplotlib.pyplot as plt
import os, gc, cv2, random, warnings, math, sys, json, pprint, pdb
import tensorflow as tf
from tensorflow.keras import backend as K
import tensorflow_hub as hub
from sklearn.model_selection import train_test_split
warnings.simplefilter('ignore')
print(f"Using TensorFlow v{tf.__version__}")
#@title Accelerator type { run: "auto", display-mode: "form" }
DEVICE = 'GPU' #@param ["None", "'GPU'", "'TPU'"] {type:"raw", allow-input: true}
if DEVICE == "TPU":
print("connecting to TPU...")
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
print('Running on TPU ', tpu.master())
except ValueError:
print("Could not connect to TPU")
tpu = None
if tpu:
try:
print("initializing TPU ...")
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
strategy = tf.distribute.experimental.TPUStrategy(tpu)
print("TPU initialized")
except _:
print("failed to initialize TPU")
else:
DEVICE = "GPU"
if DEVICE != "TPU":
print("Using default strategy for CPU and single GPU")
strategy = tf.distribute.get_strategy()
if DEVICE == "GPU":
print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))
AUTOTUNE = tf.data.experimental.AUTOTUNE
REPLICAS = strategy.num_replicas_in_sync
print(f'REPLICAS: {REPLICAS}')
def seed_everything(seed=0):
random.seed(seed)
np.random.seed(seed)
tf.random.set_seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
os.environ['TF_DETERMINISTIC_OPS'] = '1'
GOOGLE = 'google.colab' in str(get_ipython())
KAGGLE = not GOOGLE
print("Running on {}!".format(
"Google Colab" if GOOGLE else "Kaggle Kernel"
))
Tip: Adding seed helps reproduce results. Setting debug parameter wil run the model on smaller number of epochs to validate the architecture.
#@title ML Liftcycle { run: "auto", display-mode:"form" }
SEED = 16
DEBUG = False #@param {type:"boolean"}
TRAIN = True #@param {type:"boolean"}
INFERENCE = True #@param {type:"boolean"}
seed_everything(SEED)
%%run_if {GOOGLE}
from google.colab import drive
drive.mount('/content/gdrive', force_remount=True)
project_name = 'cassava-leaf-disease-classification'
root_path = '/content/gdrive/MyDrive/' if GOOGLE else '/'
input_path = f'{root_path}kaggle/input/{project_name}/'
working_path = f'{input_path}working/' if GOOGLE else '/kaggle/working/'
os.makedirs(working_path, exist_ok=True)
os.chdir(working_path)
os.listdir(input_path)
BASE_MODEL, IMG_SIZE = ('efficientnet_b3', 300) #@param ["('efficientnet_b3', 300)", "('efficientnet_b4', 380)", "('efficientnet_b2', 260)"] {type:"raw", allow-input: true}
BATCH_SIZE = 32 #@param {type:"integer"}
IMG_SIZE = (IMG_SIZE, IMG_SIZE, 3) #@param ["(IMG_SIZE, IMG_SIZE, 3)", "(512,512, 3)"] {type:"raw"}
print("Using {} with input size {}".format(BASE_MODEL, IMG_SIZE))
df = pd.read_csv(f'{input_path}train.csv')
df.head()
Check how many images are available in the training dataset and also check if each item in the training set are unique
print(f"There are {len(df)} train images")
len(df.image_id) == len(df.image_id.unique())
(df.label.value_counts(normalize=True) * 100).plot.barh(figsize = (8, 5))
df['filename'] = df['image_id'].map(lambda x : f'{input_path}train_images/{x}')
df = df.drop(columns = ['image_id'])
df = df.sample(frac=1).reset_index(drop=True)
df.head()
if DEBUG:
_, df = train_test_split(
df,
test_size = 0.1,
random_state=SEED,
shuffle=True,
stratify=df['label'])
In this case, we have 5 labels (4 diseases and healthy):
with open(f'{input_path}label_num_to_disease_map.json') as file:
id2label = json.loads(file.read())
id2label
From the bar chart shown earlier, the label 3, Cassava Mosaic Disease (CMD) is the most common one. This imbalance may have to be addressed with a weighted loss function or oversampling. I might try this in a future iteration of this kernel or in a new kernel.
Let's check an example image to see what it looks like
from PIL import Image
img = Image.open(df[df.label==3]['filename'].iloc[0])
width, height = img.size
print(f"The size of the image is W{width} x H{height}")
img
Loading data
After my quick and rough EDA, let's load the PIL Image to a Numpy array, so we can move on to data augmentation.
In fastai, they have item_tfms and batch_tfms defined for their data loader API. The item transforms performs a fairly large crop to 224 and also apply other standard augmentations (in aug_tranforms) at the batch level on the GPU. The batch size is set to 32 here.
Split the dataset into training set and validation set
train_df, valid_df = train_test_split(
df
,test_size = 0.2
,random_state = SEED
,shuffle = True
,stratify = df['label'])
train_ds = tf.data.Dataset.from_tensor_slices(
(train_df.filename.values,train_df.label.values))
valid_ds = tf.data.Dataset.from_tensor_slices(
(valid_df.filename.values, valid_df.label.values))
adapt_ds = tf.data.Dataset.from_tensor_slices(
train_df.filename.values)
for x,y in valid_ds.take(3): print(x, y)
Important: You may have noticed that I had not used any kind of normalization or rescaling. I recently discovered that there is Normalization layer included in Keras’ pretrained EfficientNet, as mentioned here.
def decode_image(filename):
img = tf.io.read_file(filename)
img = tf.image.decode_jpeg(img, channels=3)
return img
def collate_train(filename, label):
img = decode_image(filename)
img = tf.image.random_brightness(img, 0.3)
img = tf.image.random_flip_left_right(img, seed=None)
img = tf.image.random_crop(img, IMG_SIZE)
return img, label
def process_adapt(filename):
img = decode_image(filename)
img = tf.keras.layers.experimental.preprocessing.Rescaling(1.0 / 255)(img)
return img
def collate_valid(filename, label):
img = decode_image(filename)
img = tf.image.random_crop(img, IMG_SIZE)
return img, label
train_ds = train_ds.map(collate_train, num_parallel_calls=AUTOTUNE)
valid_ds = valid_ds.map(collate_valid, num_parallel_calls=AUTOTUNE)
adapt_ds = adapt_ds.map(process_adapt, num_parallel_calls=AUTOTUNE)
train_ds_batch = (train_ds
.cache('dump.tfcache')
.shuffle(buffer_size=1000)
.batch(BATCH_SIZE)
.prefetch(buffer_size=AUTOTUNE))
valid_ds_batch = (valid_ds
#.shuffle(buffer_size=1000)
.batch(BATCH_SIZE*2)
.prefetch(buffer_size=AUTOTUNE))
adapt_ds_batch = (adapt_ds
.shuffle(buffer_size=1000)
.batch(BATCH_SIZE)
.prefetch(buffer_size=AUTOTUNE))
def show_images(ds):
_,axs = plt.subplots(3,3,figsize=(16,16))
for ((x, y), ax) in zip(ds.take(9), axs.flatten()):
ax.imshow(x.numpy().astype(np.uint8))
ax.set_title(np.argmax(y))
ax.axis('off')
show_images(train_ds)
show_images(valid_ds)
data_augmentation = tf.keras.Sequential(
[
tf.keras.layers.experimental.preprocessing.RandomCrop(*IMG_SIZE),
tf.keras.layers.experimental.preprocessing.RandomFlip("horizontal_and_vertical"),
tf.keras.layers.experimental.preprocessing.RandomRotation(0.25),
tf.keras.layers.experimental.preprocessing.RandomZoom((-0.2, 0)),
tf.keras.layers.experimental.preprocessing.RandomContrast((0.2,0.2))
]
)
func = lambda x,y: (data_augmentation(x), y)
x = (train_ds
.batch(BATCH_SIZE)
.take(1)
.map(func, num_parallel_calls=AUTOTUNE))
show_images(x.unbatch())
%%run_if {KAGGLE}
subprocess.check_call([sys.executable, '-m', 'pip', 'install','-q', 'efficientnet'])
from efficientnet.tfkeras import EfficientNetB3
%%run_if {GOOGLE}
from tensorflow.keras.applications import EfficientNetB3
from tensorflow.keras.applications import VGG16
def build_model(base_model, num_class):
inputs = tf.keras.layers.Input(shape=IMG_SIZE)
x = data_augmentation(inputs)
x = base_model(x)
x = tf.keras.layers.Dropout(0.4)(x)
outputs = tf.keras.layers.Dense(num_class, activation="softmax", name="pred")(x)
model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
return model
efficientnet = EfficientNetB3(
weights = 'imagenet' if TRAIN else None,
include_top = False,
input_shape = IMG_SIZE,
pooling='avg')
efficientnet.trainable = True
model = build_model(base_model=efficientnet, num_class=len(id2label))
model.summary()
The 3rd layer of the Efficient is the Normalization layer, which can be tuned to our new dataset instead of imagenet. Be patient on this one, it does take a bit of time as we're going through the entire training set.
%%run_if {GOOGLE}
if TRAIN:
if not os.path.exists("000_normalization.h5"):
model.get_layer('efficientnetb3').get_layer('normalization').adapt(adapt_ds_batch)
model.save_weights("000_normalization.h5")
else:
model.load_weights("000_normalization.h5")
class MultiplicativeLearningRate(tf.keras.callbacks.Callback):
def __init__(self, factor):
self.factor = factor
self.losses = []
self.lrs = []
def on_batch_end(self, batch, logs):
self.lrs.append(K.get_value(self.model.optimizer.lr))
self.losses.append(logs["loss"])
K.set_value(self.model.optimizer.lr, self.model.optimizer.lr*self.factor)
def find_lr(model, ds, batch_size, num_iter, min_lr=1e-6, max_lr=1e1):
num_iter = len(x) // batch_size
lr_factor = np.exp(np.log(max_lr / min_lr) / num_iter)
# Train for 1 epoch, starting with minimum learning rate and increase it
K.set_value(model.optimizer.lr, min_lr)
lr_callback = MultiplicativeLearningRate(lr_factor)
model.fit(ds, epochs=1, batch_size=batch_size, callbacks=[lr_callback])
# Plot loss vs log-scaled learning rate
plot = sns.lineplot(lr_callback.lrs, lr_callback.losses)
plot.set(xscale="log",
xlabel="Learning Rate (log-scale)",
ylabel="Training Loss",
title="Optimal learning rate is slightly below minimum",
facecolor="#F0F0F0")
model.compile(loss="sparse_categorical_crossentropy",
optimizer=tf.keras.optimizers.Adam(schedule),
metrics=["accuracy"])
find_lr(model, train_ds_batch, num_iter=len(train_df)//BATCH_SIZE, batch_size=BATCH_SIZE)
Important: I always wanted to try the new
CosineDecayRestarts function implemented in tf.keras as it seemed promising and I struggled to find the right settings (if there were any) for the ReduceLROnPlateau
EPOCHS = 8
STEPS = int(round(len(train_df)/BATCH_SIZE)) * EPOCHS
schedule = tf.keras.experimental.CosineDecayRestarts(
initial_learning_rate=1e-4,
first_decay_steps=300
)
schedule.get_config()
x = [i for i in range(STEPS)]
y = [schedule(s) for s in range(STEPS)]
plt.plot(x, y)
Warning: There is a gap between what I had expected and the acutal
LearningRateScheduler that tensorflow gives us. The LearningRateScheduler update the lr on_epoch_begin while it makes more sense to do it on_batch_end or on_batch_begin.
callbacks = [
tf.keras.callbacks.ModelCheckpoint(
filepath='001_best_model.h5',
monitor='val_loss',
save_best_only=True),
]
model.compile(loss="sparse_categorical_crossentropy",
optimizer=tf.keras.optimizers.Adam(schedule),
metrics=["accuracy"])
if TRAIN:
history = model.fit(train_ds_batch,
epochs = EPOCHS,
validation_data=valid_ds_batch,
callbacks=callbacks)
def plot_hist(hist):
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Loss over epochs')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'valid'], loc='best')
plt.show()
if TRAIN:
plot_hist(history)
We load the best weight that were kept from the training phase. Just to check how our model is performing, we will attempt predictions over the validation set. This can help to highlight any classes that will be consistently miscategorised.
model.load_weights('001_best_model.h5')
x = train_df.sample(1).filename.values[0]
img = decode_image(x)
%%time
imgs = [tf.image.random_crop(img, size=[*IMG_SIZE, 3]) for _ in range(4)]
_,axs = plt.subplots(1,4,figsize=(16,4))
for (x, ax) in zip(imgs, axs.flatten()):
ax.imshow(x.numpy().astype(np.uint8))
ax.axis('off')
I apply some very basic test time augmentation to every local image extracted from the original 600-by-800 images. We know we can do some fancy augmentation with albumentations but I wanted to do that exclusively with Keras preprocessing layers to keep the cleanest pipeline possible.
tta = tf.keras.Sequential(
[
tf.keras.layers.experimental.preprocessing.RandomCrop((*IMG_SIZE)),
tf.keras.layers.experimental.preprocessing.RandomFlip("horizontal_and_vertical"),
tf.keras.layers.experimental.preprocessing.RandomZoom((-0.2, 0.2)),
tf.keras.layers.experimental.preprocessing.RandomContrast((0.2,0.2))
]
)
def predict_tta(filename, num_tta=4):
img = decode_image(filename)
img = tf.expand_dims(img, 0)
imgs = tf.concat([tta(img) for _ in range(num_tta)], 0)
preds = model.predict(imgs)
return preds.sum(0).argmax()
pred = predict_tta(df.sample(1).filename.values[0])
print(pred)
if INFERENCE:
from tqdm import tqdm
preds = []
with tqdm(total=len(valid_df)) as pbar:
for filename in valid_df.filename:
pbar.update()
preds.append(predict_tta(filename, num_tta=4))
if INFERENCE:
cm = tf.math.confusion_matrix(valid_df.label.values, np.array(preds))
plt.figure(figsize=(10, 8))
sns.heatmap(cm,
xticklabels=id2label.values(),
yticklabels=id2label.values(),
annot=True,
fmt='g',
cmap="Blues")
plt.xlabel('Prediction')
plt.ylabel('Label')
plt.show()
test_folder = input_path + '/test_images/'
submission_df = pd.DataFrame(columns={"image_id","label"})
submission_df["image_id"] = os.listdir(test_folder)
submission_df["label"] = 0
submission_df['label'] = (submission_df['image_id']
.map(lambda x : predict_tta(test_folder+x)))
submission_df
submission_df.to_csv("submission.csv", index=False)
1% Better Everyday
reference
- https://www.kaggle.com/c/cassava-leaf-disease-classification
- https://www.kaggle.com/dimitreoliveira/cassava-leaf-disease-training-with-tpu-v2-pods/notebook#Training-data-samples-(with-augmentation)
- https://keras.io/examples/vision/image_classification_efficientnet_fine_tuning/#keras-implementation-of-efficientnet
- https://www.tensorflow.org/guide/gpu_performance_analysis
- https://www.tensorflow.org/guide/data_performance#prefetching
- https://www.tensorflow.org/guide/data_performance_analysis
todos
- See if I can integrate the Cutmix/Mixup augmentations in the appendix into our existing notebook. This is an excellent example
- Still want to figure out some intuition of item aug and batch aug. I don't know, maybe there is some limitation or how to do so to help to speed up.
- Learn more about the
adaptfunction that being used to retrain the normalization layer of the EfficientNetB3.
done
- Predict in batch to speed up
- Add a cell for checkbox parameter to select between kaggle and colab, default is
Kaggle. - Try out the
data_generatorand thedata_frame_iterator - Removing normalizaiton step in generator since in EfficientNet, normalization is done within the model itself and the model expects input in the range of [0,255]
-
Find out the intuition and the difference between
item_tfmandbatch_tfmIn fastai,
item_tfmdefines the transforms that are done on the CPU andbatch_tfmdefines those done on the GPU. -
Customize my own data generator as fastai creates their
DataloaderNo need, things are much easier than what I was originally expecting. Please refer to the
Loading datasection in this notebook. -
The 3rd layer of the Efficientnet is the Normalization layer, which can be tuned to our new dataset instead of
imagenet. Be patient on this one, it does take a bit of time we're going through the entire training set. - Add
seed_everythingfunction
def albu_transforms_train(data_resize):
return A.Compose([
A.ToFloat(),
A.Resize(data_resize, data_resize),
], p=1.)
# For Validation
def albu_transforms_valid(data_resize):
return A.Compose([
A.ToFloat(),
A.Resize(data_resize, data_resize),
], p=1.)
def CutMix(image, label, DIM, PROBABILITY = 1.0):
# input image - is a batch of images of size [n,dim,dim,3] not a single image of [dim,dim,3]
# output - a batch of images with cutmix applied
CLASSES = 5
imgs = []; labs = []
for j in range(len(image)):
# DO CUTMIX WITH PROBABILITY DEFINED ABOVE
P = tf.cast( tf.random.uniform([],0,1)<=PROBABILITY, tf.int32)
# CHOOSE RANDOM IMAGE TO CUTMIX WITH
k = tf.cast( tf.random.uniform([],0,len(image)),tf.int32)
# CHOOSE RANDOM LOCATION
x = tf.cast( tf.random.uniform([],0,DIM),tf.int32)
y = tf.cast( tf.random.uniform([],0,DIM),tf.int32)
b = tf.random.uniform([],0,1) # this is beta dist with alpha=1.0
WIDTH = tf.cast( DIM * tf.math.sqrt(1-b),tf.int32) * P
ya = tf.math.maximum(0,y-WIDTH//2)
yb = tf.math.minimum(DIM,y+WIDTH//2)
xa = tf.math.maximum(0,x-WIDTH//2)
xb = tf.math.minimum(DIM,x+WIDTH//2)
# MAKE CUTMIX IMAGE
one = image[j,ya:yb,0:xa,:]
two = image[k,ya:yb,xa:xb,:]
three = image[j,ya:yb,xb:DIM,:]
middle = tf.concat([one,two,three],axis=1)
img = tf.concat([image[j,0:ya,:,:],middle,image[j,yb:DIM,:,:]],axis=0)
imgs.append(img)
# MAKE CUTMIX LABEL
a = tf.cast(WIDTH*WIDTH/DIM/DIM,tf.float32)
labs.append((1-a)*label[j] + a*label[k])
# RESHAPE HACK SO TPU COMPILER KNOWS SHAPE OF OUTPUT TENSOR (maybe use Python typing instead?)
image2 = tf.reshape(tf.stack(imgs),(len(image),DIM,DIM,3))
label2 = tf.reshape(tf.stack(labs),(len(image),CLASSES))
return image2,label2
def MixUp(image, label, DIM, PROBABILITY = 1.0):
# input image - is a batch of images of size [n,dim,dim,3] not a single image of [dim,dim,3]
# output - a batch of images with mixup applied
CLASSES = 5
imgs = []; labs = []
for j in range(len(image)):
# DO MIXUP WITH PROBABILITY DEFINED ABOVE
P = tf.cast( tf.random.uniform([],0,1)<=PROBABILITY, tf.float32)
# CHOOSE RANDOM
k = tf.cast( tf.random.uniform([],0,len(image)),tf.int32)
a = tf.random.uniform([],0,1)*P # this is beta dist with alpha=1.0
# MAKE MIXUP IMAGE
img1 = image[j,]
img2 = image[k,]
imgs.append((1-a)*img1 + a*img2)
# MAKE CUTMIX LABEL
labs.append((1-a)*label[j] + a*label[k])
# RESHAPE HACK SO TPU COMPILER KNOWS SHAPE OF OUTPUT TENSOR (maybe use Python typing instead?)
image2 = tf.reshape(tf.stack(imgs),(len(image),DIM,DIM,3))
label2 = tf.reshape(tf.stack(labs),(len(image),CLASSES))
return image2,label2